Mathematical Foundations for Machine Learning

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Curated by: NPTEL - Indian Institute of Science, Bengaluru (71 videos)

Currently Playing: Lec 59 Revisiting Least squares, Principal Component Analysis

Least square solution as an optimization problem, PCA as a constrained optimization problem, Using Lagrange multiplier to get the direction of maximum variance

Tracks in this Playlist

Lec 70 Course Summary, Credits & Acknowledgments
Lec 69 Logistic Regression
Lec 68 Linear Regression : Line of best fit
Lec 67 Classification, Clustering Techniques
Lec 66 Classification and a simple binary classifier
Lec 65 Data and Patterns
Lec 64 Back Propogation Algorithm
Lec 63 Multilayer Layer Perceptron
Lec 62 Neural Networks , Perceptron
Lec 61 Variants of the Gradient Descent Algorithm
Lec 60 Minimizing the Cost Function: Gradent Descent Algorithm
Lec 59 Revisiting Least squares, Principal Component Analysis
Lec 58 Constrained optimization, Lagrange Multiplier
Lec 57 Constrained Optimization, Optimal solutions, Saddle point
Lec 56 Optimization Overview
Lec 55 Matrix derivatives
Lec 54 Rules for Partial Derivatives, Jacobian and Hessian
Lec 53 Gradient and Directional Derivatives
Lec 52 Multivariate functions
Lec 51 Differentiation Rules
Lec 50 Functions, Derivatives, Infinite Series
Lec 49 Covariance Matrix & its properties
Lec 48 Sample Geometry
Lec 47 Central Limit Theorem
Lec 46 Chebychev Inequality
Lec 45 Markov Inequality
Lec 44 Joint Moments of Continuous random Variables and Conditioning of Random Variables
Lec 43 Correlation and Covariance
Lec 42 Independence and Correlation
Lec 41 Joint Moments of Random Variables
Lec 40 Joint Distributions and Marginals
Lec 39 Moments and Variance
Lec 38 Expected Value of Random Variable
Lec 37 Continuous Random Variables
Lec 36 Types of Discrete Random Variables and Their Probability Distributions
Lec 35 Discrete Random Variables and Probability Mass Function (PMF)
Lec 34 Random Experiment and Random Variables
Lec 33 Probability - A Measure Theoretic Insight
Lec 32 Total Probability Theorem and Bayes' Theorem
Lec 31 Mutually Exclusive Events, Independent Events and Conditional Probability
Lec 30 Introduction to Probability - 2
Lec 29 Introduction to Probability - 1
Lec 28 Consolidating Week1 to Week 4
Lec 27 Support Vector Machine (SVM)
Lec 26 Singular Value Decomposition (SVD) Interpretation
Lec 25 Singular Value Decomposition (SVD)
Lec 24 Principal Component Analysis (PCA)
Lec 23 Least Square Fitting and Pseudo Inverse - 2
Lec 22 Least Square Fitting and Pseudo Inverse - 1
Lec 21 Real Symmetric Matrices
Lec 20 Approximating a vector in any given subspace
Lec 19 Construction of Orthogonal Basis
Lec 18 Orthogonal Projections
Lec 17 Orthonormal Basis
Lec 16 Orthogonality on Vector Space
Lec 15 Dot Product on Vector Space
Lec 14 Diagonalizability, Invariant subspaces
Lec 13 Diagonalization of Matrix
Lec 12 Multiplicity of Eigen Values
Lec 11 Eigenvalues and Eigenvectors
Lec 10 Examples of Linear Transformation
Lec 09 Matrix representation of Linear Transformation
Lec 08 Subspaces associated with Linear Transformations
Lec 07 Linear Transformation - Intuition
Lec 06 Basis and Dimension
Lec 05 Combining Vectors and Linear Independence
Lec 04 Vector Spaces and Subspaces
Lec 03 Fields, Vector Spaces
Lec 02 Fields and Their Properties – Algebraic Tools for ML
Lec 01 Why this course?
Mathematical Foundations for Machine Learning (Introduction Video)